Not sure if this post belongs here or if stack overflow would be more appropriate.
I am starting to familiarize with the caret package in R which seems very powerful for the purpose of optimizing and implementing various machine learning methods. According to my understanding the key idea of the package is to train a model across different parameter sets and resampling methods and to select the optimal calibration based on a certain performance measure. This optimized model can subsequently be used to compute predictions on the test data.
Does the package also allow computing predictions for all trained models other than the optimal model?
If this is possible a minimum working example would be nice, but not essential.
The reason for my question is that I am interested in checking the predictive performance of the optimal model relative to the other trained models on the test data. Moreover, I would like to evaluate the performance of forecast combination schemes based on multiple model calibrations.